Tag Archives: brainlike computing

Announcing the ‘memtransistor’

Yet another advance toward ‘brainlike’ computing (how many times have I written this or a variation thereof in the last 10 years? See: Dexter Johnson’s take on the situation at the end of this post): Northwestern University announced their latest memristor research in a February 21, 2018 news item on Nanowerk,

Computer algorithms might be performing brain-like functions, such as facial recognition and language translation, but the computers themselves have yet to operate like brains.

“Computers have separate processing and memory storage units, whereas the brain uses neurons to perform both functions,” said Northwestern University’s Mark C. Hersam. “Neural networks can achieve complicated computation with significantly lower energy consumption compared to a digital computer.”

A February 21, 2018 Northwestern University news release (also on EurekAlert), which originated the news item, provides more information about the latest work from this team,

In recent years, researchers have searched for ways to make computers more neuromorphic, or brain-like, in order to perform increasingly complicated tasks with high efficiency. Now Hersam, a Walter P. Murphy Professor of Materials Science and Engineering in Northwestern’s McCormick School of Engineering, and his team are bringing the world closer to realizing this goal.

The research team has developed a novel device called a “memtransistor,” which operates much like a neuron by performing both memory and information processing. With combined characteristics of a memristor and transistor, the memtransistor also encompasses multiple terminals that operate more similarly to a neural network.

Supported by the National Institute of Standards and Technology and the National Science Foundation, the research was published online today, February 22 [2018], in Nature. Vinod K. Sangwan and Hong-Sub Lee, postdoctoral fellows advised by Hersam, served as the paper’s co-first authors.

The memtransistor builds upon work published in 2015, in which Hersam, Sangwan, and their collaborators used single-layer molybdenum disulfide (MoS2) to create a three-terminal, gate-tunable memristor for fast, reliable digital memory storage. Memristor, which is short for “memory resistors,” are resistors in a current that “remember” the voltage previously applied to them. Typical memristors are two-terminal electronic devices, which can only control one voltage channel. By transforming it into a three-terminal device, Hersam paved the way for memristors to be used in more complex electronic circuits and systems, such as neuromorphic computing.

To develop the memtransistor, Hersam’s team again used atomically thin MoS2 with well-defined grain boundaries, which influence the flow of current. Similar to the way fibers are arranged in wood, atoms are arranged into ordered domains – called “grains” – within a material. When a large voltage is applied, the grain boundaries facilitate atomic motion, causing a change in resistance.

“Because molybdenum disulfide is atomically thin, it is easily influenced by applied electric fields,” Hersam explained. “This property allows us to make a transistor. The memristor characteristics come from the fact that the defects in the material are relatively mobile, especially in the presence of grain boundaries.”

But unlike his previous memristor, which used individual, small flakes of MoS2, Hersam’s memtransistor makes use of a continuous film of polycrystalline MoS2 that comprises a large number of smaller flakes. This enabled the research team to scale up the device from one flake to many devices across an entire wafer.

“When length of the device is larger than the individual grain size, you are guaranteed to have grain boundaries in every device across the wafer,” Hersam said. “Thus, we see reproducible, gate-tunable memristive responses across large arrays of devices.”

After fabricating memtransistors uniformly across an entire wafer, Hersam’s team added additional electrical contacts. Typical transistors and Hersam’s previously developed memristor each have three terminals. In their new paper, however, the team realized a seven-terminal device, in which one terminal controls the current among the other six terminals.

“This is even more similar to neurons in the brain,” Hersam said, “because in the brain, we don’t usually have one neuron connected to only one other neuron. Instead, one neuron is connected to multiple other neurons to form a network. Our device structure allows multiple contacts, which is similar to the multiple synapses in neurons.”

Next, Hersam and his team are working to make the memtransistor faster and smaller. Hersam also plans to continue scaling up the device for manufacturing purposes.

“We believe that the memtransistor can be a foundational circuit element for new forms of neuromorphic computing,” he said. “However, making dozens of devices, as we have done in our paper, is different than making a billion, which is done with conventional transistor technology today. Thus far, we do not see any fundamental barriers that will prevent further scale up of our approach.”

The researchers have made this illustration available,

Caption: This is the memtransistor symbol overlaid on an artistic rendering of a hypothetical circuit layout in the shape of a brain. Credit; Hersam Research Group

Here’s a link to and a citation for the paper,

Multi-terminal memtransistors from polycrystalline monolayer molybdenum disulfide by Vinod K. Sangwan, Hong-Sub Lee, Hadallia Bergeron, Itamar Balla, Megan E. Beck, Kan-Sheng Chen, & Mark C. Hersam. Nature volume 554, pages 500–504 (22 February 2018 doi:10.1038/nature25747 Published online: 21 February 2018

This paper is behind a paywall.

The team’s earlier work referenced in the news release was featured here in an April 10, 2015 posting.

Dexter Johnson

From a Feb. 23, 2018 posting by Dexter Johnson on the Nanoclast blog (on the IEEE [Institute of Electrical and Electronics Engineers] website),

While this all seems promising, one of the big shortcomings in neuromorphic computing has been that it doesn’t mimic the brain in a very important way. In the brain, for every neuron there are a thousand synapses—the electrical signal sent between the neurons of the brain. This poses a problem because a transistor only has a single terminal, hardly an accommodating architecture for multiplying signals.

Now researchers at Northwestern University, led by Mark Hersam, have developed a new device that combines memristors—two-terminal non-volatile memory devices based on resistance switching—with transistors to create what Hersam and his colleagues have dubbed a “memtransistor” that performs both memory storage and information processing.

This most recent research builds on work that Hersam and his team conducted back in 2015 in which the researchers developed a three-terminal, gate-tunable memristor that operated like a kind of synapse.

While this work was recognized as mimicking the low-power computing of the human brain, critics didn’t really believe that it was acting like a neuron since it could only transmit a signal from one artificial neuron to another. This was far short of a human brain that is capable of making tens of thousands of such connections.

“Traditional memristors are two-terminal devices, whereas our memtransistors combine the non-volatility of a two-terminal memristor with the gate-tunability of a three-terminal transistor,” said Hersam to IEEE Spectrum. “Our device design accommodates additional terminals, which mimic the multiple synapses in neurons.”

Hersam believes that these unique attributes of these multi-terminal memtransistors are likely to present a range of new opportunities for non-volatile memory and neuromorphic computing.

If you have the time and the interest, Dexter’s post provides more context,

Less is more—a superconducting synapse

It seems the US National Institute of Standards and Technology (NIST) is more deeply invested into developing artificial brains than I had realized (See: April 17, 2018 posting). A January 26, 2018 NIST news release on EurekAlert describes the organization’s latest foray into the field,

Researchers at the National Institute of Standards and Technology (NIST) have built a superconducting switch that “learns” like a biological system and could connect processors and store memories in future computers operating like the human brain.

The NIST switch, described in Science Advances, is called a synapse, like its biological counterpart, and it supplies a missing piece for so-called neuromorphic computers. Envisioned as a new type of artificial intelligence, such computers could boost perception and decision-making for applications such as self-driving cars and cancer diagnosis.

A synapse is a connection or switch between two brain cells. NIST’s artificial synapse–a squat metallic cylinder 10 micrometers in diameter–is like the real thing because it can process incoming electrical spikes to customize spiking output signals. This processing is based on a flexible internal design that can be tuned by experience or its environment. The more firing between cells or processors, the stronger the connection. Both the real and artificial synapses can thus maintain old circuits and create new ones. Even better than the real thing, the NIST synapse can fire much faster than the human brain–1 billion times per second, compared to a brain cell’s 50 times per second–using just a whiff of energy, about one ten-thousandth as much as a human synapse. In technical terms, the spiking energy is less than 1 attojoule, lower than the background energy at room temperature and on a par with the chemical energy bonding two atoms in a molecule.

“The NIST synapse has lower energy needs than the human synapse, and we don’t know of any other artificial synapse that uses less energy,” NIST physicist Mike Schneider said.

The new synapse would be used in neuromorphic computers made of superconducting components, which can transmit electricity without resistance, and therefore, would be more efficient than other designs based on semiconductors or software. Data would be transmitted, processed and stored in units of magnetic flux. Superconducting devices mimicking brain cells and transmission lines have been developed, but until now, efficient synapses–a crucial piece–have been missing.

The brain is especially powerful for tasks like context recognition because it processes data both in sequence and simultaneously and stores memories in synapses all over the system. A conventional computer processes data only in sequence and stores memory in a separate unit.

The NIST synapse is a Josephson junction, long used in NIST voltage standards. These junctions are a sandwich of superconducting materials with an insulator as a filling. When an electrical current through the junction exceeds a level called the critical current, voltage spikes are produced. The synapse uses standard niobium electrodes but has a unique filling made of nanoscale clusters of manganese in a silicon matrix.

The nanoclusters–about 20,000 per square micrometer–act like tiny bar magnets with “spins” that can be oriented either randomly or in a coordinated manner.

“These are customized Josephson junctions,” Schneider said. “We can control the number of nanoclusters pointing in the same direction, which affects the superconducting properties of the junction.”

The synapse rests in a superconducting state, except when it’s activated by incoming current and starts producing voltage spikes. Researchers apply current pulses in a magnetic field to boost the magnetic ordering, that is, the number of nanoclusters pointing in the same direction. This magnetic effect progressively reduces the critical current level, making it easier to create a normal conductor and produce voltage spikes.

The critical current is the lowest when all the nanoclusters are aligned. The process is also reversible: Pulses are applied without a magnetic field to reduce the magnetic ordering and raise the critical current. This design, in which different inputs alter the spin alignment and resulting output signals, is similar to how the brain operates.

Synapse behavior can also be tuned by changing how the device is made and its operating temperature. By making the nanoclusters smaller, researchers can reduce the pulse energy needed to raise or lower the magnetic order of the device. Raising the operating temperature slightly from minus 271.15 degrees C (minus 456.07 degrees F) to minus 269.15 degrees C (minus 452.47 degrees F), for example, results in more and higher voltage spikes.

Crucially, the synapses can be stacked in three dimensions (3-D) to make large systems that could be used for computing. NIST researchers created a circuit model to simulate how such a system would operate.

The NIST synapse’s combination of small size, superfast spiking signals, low energy needs and 3-D stacking capability could provide the means for a far more complex neuromorphic system than has been demonstrated with other technologies, according to the paper.

NIST has prepared an animation illustrating the research,

Caption: This is an animation of how NIST’s artificial synapse works. Credit: Sean Kelley/NIST

Here’s a link to and a citation for the paper,

Ultralow power artificial synapses using nanotextured magnetic Josephson junctions by Michael L. Schneider, Christine A. Donnelly, Stephen E. Russek, Burm Baek, Matthew R. Pufall, Peter F. Hopkins, Paul D. Dresselhaus, Samuel P. Benz, and William H. Rippard. Science Advances 26 Jan 2018: Vol. 4, no. 1, e1701329 DOI: 10.1126/sciadv.1701329

This paper is open access.

Samuel K. Moore in a January 26, 2018 posting on the Nanoclast blog (on the IEEE [Institute for Electrical and Electronics Engineers] website) describes the research and adds a few technical explanations such as this about the Josephson junction,

In a magnetic Josephson junction, that “weak link” is magnetic. The higher the magnetic field, the lower the critical current needed to produce voltage spikes. In the device Schneider and his colleagues designed, the magnetic field is caused by 20,000 or so nanometer-scale clusters of manganese embedded in silicon. …

Moore also provides some additional links including this one to his November 29, 2017 posting where he describes four new approaches to computing including quantum computing and neuromorphic (brain-like) computing.

New path to viable memristor/neuristor?

I first stumbled onto memristors and the possibility of brain-like computing sometime in 2008 (around the time that R. Stanley Williams and his team at HP Labs first published the results of their research linking Dr. Leon Chua’s memristor theory to their attempts to shrink computer chips). In the almost 10 years since, scientists have worked hard to utilize memristors in the field of neuromorphic (brain-like) engineering/computing.

A January 22, 2018 news item on phys.org describes the latest work,

When it comes to processing power, the human brain just can’t be beat.

Packed within the squishy, football-sized organ are somewhere around 100 billion neurons. At any given moment, a single neuron can relay instructions to thousands of other neurons via synapses—the spaces between neurons, across which neurotransmitters are exchanged. There are more than 100 trillion synapses that mediate neuron signaling in the brain, strengthening some connections while pruning others, in a process that enables the brain to recognize patterns, remember facts, and carry out other learning tasks, at lightning speeds.

Researchers in the emerging field of “neuromorphic computing” have attempted to design computer chips that work like the human brain. Instead of carrying out computations based on binary, on/off signaling, like digital chips do today, the elements of a “brain on a chip” would work in an analog fashion, exchanging a gradient of signals, or “weights,” much like neurons that activate in various ways depending on the type and number of ions that flow across a synapse.

In this way, small neuromorphic chips could, like the brain, efficiently process millions of streams of parallel computations that are currently only possible with large banks of supercomputers. But one significant hangup on the way to such portable artificial intelligence has been the neural synapse, which has been particularly tricky to reproduce in hardware.

Now engineers at MIT [Massachusetts Institute of Technology] have designed an artificial synapse in such a way that they can precisely control the strength of an electric current flowing across it, similar to the way ions flow between neurons. The team has built a small chip with artificial synapses, made from silicon germanium. In simulations, the researchers found that the chip and its synapses could be used to recognize samples of handwriting, with 95 percent accuracy.

A January 22, 2018 MIT news release by Jennifer Chua (also on EurekAlert), which originated the news item, provides more detail about the research,

The design, published today [January 22, 2018] in the journal Nature Materials, is a major step toward building portable, low-power neuromorphic chips for use in pattern recognition and other learning tasks.

The research was led by Jeehwan Kim, the Class of 1947 Career Development Assistant Professor in the departments of Mechanical Engineering and Materials Science and Engineering, and a principal investigator in MIT’s Research Laboratory of Electronics and Microsystems Technology Laboratories. His co-authors are Shinhyun Choi (first author), Scott Tan (co-first author), Zefan Li, Yunjo Kim, Chanyeol Choi, and Hanwool Yeon of MIT, along with Pai-Yu Chen and Shimeng Yu of Arizona State University.

Too many paths

Most neuromorphic chip designs attempt to emulate the synaptic connection between neurons using two conductive layers separated by a “switching medium,” or synapse-like space. When a voltage is applied, ions should move in the switching medium to create conductive filaments, similarly to how the “weight” of a synapse changes.

But it’s been difficult to control the flow of ions in existing designs. Kim says that’s because most switching mediums, made of amorphous materials, have unlimited possible paths through which ions can travel — a bit like Pachinko, a mechanical arcade game that funnels small steel balls down through a series of pins and levers, which act to either divert or direct the balls out of the machine.

Like Pachinko, existing switching mediums contain multiple paths that make it difficult to predict where ions will make it through. Kim says that can create unwanted nonuniformity in a synapse’s performance.

“Once you apply some voltage to represent some data with your artificial neuron, you have to erase and be able to write it again in the exact same way,” Kim says. “But in an amorphous solid, when you write again, the ions go in different directions because there are lots of defects. This stream is changing, and it’s hard to control. That’s the biggest problem — nonuniformity of the artificial synapse.”

A perfect mismatch

Instead of using amorphous materials as an artificial synapse, Kim and his colleagues looked to single-crystalline silicon, a defect-free conducting material made from atoms arranged in a continuously ordered alignment. The team sought to create a precise, one-dimensional line defect, or dislocation, through the silicon, through which ions could predictably flow.

To do so, the researchers started with a wafer of silicon, resembling, at microscopic resolution, a chicken-wire pattern. They then grew a similar pattern of silicon germanium — a material also used commonly in transistors — on top of the silicon wafer. Silicon germanium’s lattice is slightly larger than that of silicon, and Kim found that together, the two perfectly mismatched materials can form a funnel-like dislocation, creating a single path through which ions can flow.

The researchers fabricated a neuromorphic chip consisting of artificial synapses made from silicon germanium, each synapse measuring about 25 nanometers across. They applied voltage to each synapse and found that all synapses exhibited more or less the same current, or flow of ions, with about a 4 percent variation between synapses — a much more uniform performance compared with synapses made from amorphous material.

They also tested a single synapse over multiple trials, applying the same voltage over 700 cycles, and found the synapse exhibited the same current, with just 1 percent variation from cycle to cycle.

“This is the most uniform device we could achieve, which is the key to demonstrating artificial neural networks,” Kim says.

Writing, recognized

As a final test, Kim’s team explored how its device would perform if it were to carry out actual learning tasks — specifically, recognizing samples of handwriting, which researchers consider to be a first practical test for neuromorphic chips. Such chips would consist of “input/hidden/output neurons,” each connected to other “neurons” via filament-based artificial synapses.

Scientists believe such stacks of neural nets can be made to “learn.” For instance, when fed an input that is a handwritten ‘1,’ with an output that labels it as ‘1,’ certain output neurons will be activated by input neurons and weights from an artificial synapse. When more examples of handwritten ‘1s’ are fed into the same chip, the same output neurons may be activated when they sense similar features between different samples of the same letter, thus “learning” in a fashion similar to what the brain does.

Kim and his colleagues ran a computer simulation of an artificial neural network consisting of three sheets of neural layers connected via two layers of artificial synapses, the properties of which they based on measurements from their actual neuromorphic chip. They fed into their simulation tens of thousands of samples from a handwritten recognition dataset commonly used by neuromorphic designers, and found that their neural network hardware recognized handwritten samples 95 percent of the time, compared to the 97 percent accuracy of existing software algorithms.

The team is in the process of fabricating a working neuromorphic chip that can carry out handwriting-recognition tasks, not in simulation but in reality. Looking beyond handwriting, Kim says the team’s artificial synapse design will enable much smaller, portable neural network devices that can perform complex computations that currently are only possible with large supercomputers.

“Ultimately we want a chip as big as a fingernail to replace one big supercomputer,” Kim says. “This opens a stepping stone to produce real artificial hardware.”

This research was supported in part by the National Science Foundation.

Here’s a link to and a citation for the paper,

SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations by Shinhyun Choi, Scott H. Tan, Zefan Li, Yunjo Kim, Chanyeol Choi, Pai-Yu Chen, Hanwool Yeon, Shimeng Yu, & Jeehwan Kim. Nature Materials (2018) doi:10.1038/s41563-017-0001-5 Published online: 22 January 2018

This paper is behind a paywall.

For the curious I have included a number of links to recent ‘memristor’ postings here,

January 22, 2018: Memristors at Masdar

January 3, 2018: Mott memristor

August 24, 2017: Neuristors and brainlike computing

June 28, 2017: Dr. Wei Lu and bio-inspired ‘memristor’ chips

May 2, 2017: Predicting how a memristor functions

December 30, 2016: Changing synaptic connectivity with a memristor

December 5, 2016: The memristor as computing device

November 1, 2016: The memristor as the ‘missing link’ in bioelectronic medicine?

You can find more by using ‘memristor’ as the search term in the blog search function or on the search engine of your choice.

Thanks for the memory: the US National Institute of Standards and Technology (NIST) and memristors

In January 2018 it seemed like I was tripping across a lot of memristor stories . This came from a January 19, 2018 news item on Nanowerk,

In the race to build a computer that mimics the massive computational power of the human brain, researchers are increasingly turning to memristors, which can vary their electrical resistance based on the memory of past activity. Scientists at the National Institute of Standards and Technology (NIST) have now unveiled the long-mysterious inner workings of these semiconductor elements, which can act like the short-term memory of nerve cells.

A January 18, 2018 NIST news release (also on EurekAlert), which originated the news item, fills in the details,

Just as the ability of one nerve cell to signal another depends on how often the cells have communicated in the recent past, the resistance of a memristor depends on the amount of current that recently flowed through it. Moreover, a memristor retains that memory even when electrical power is switched off.

But despite the keen interest in memristors, scientists have lacked a detailed understanding of how these devices work and have yet to develop a standard toolset to study them.

Now, NIST scientists have identified such a toolset and used it to more deeply probe how memristors operate. Their findings could lead to more efficient operation of the devices and suggest ways to minimize the leakage of current.

Brian Hoskins of NIST and the University of California, Santa Barbara, along with NIST scientists Nikolai Zhitenev, Andrei Kolmakov, Jabez McClelland and their colleagues from the University of Maryland’s NanoCenter (link is external) in College Park and the Institute for Research and Development in Microtechnologies in Bucharest, reported the findings (link is external) in a recent Nature Communications.

To explore the electrical function of memristors, the team aimed a tightly focused beam of electrons at different locations on a titanium dioxide memristor. The beam knocked free some of the device’s electrons, which formed ultrasharp images of those locations. The beam also induced four distinct currents to flow within the device. The team determined that the currents are associated with the multiple interfaces between materials in the memristor, which consists of two metal (conducting) layers separated by an insulator.

“We know exactly where each of the currents are coming from because we are controlling the location of the beam that is inducing those currents,” said Hoskins.

In imaging the device, the team found several dark spots—regions of enhanced conductivity—which indicated places where current might leak out of the memristor during its normal operation. These leakage pathways resided outside the memristor’s core—where it switches between the low and high resistance levels that are useful in an electronic device. The finding suggests that reducing the size of a memristor could minimize or even eliminate some of the unwanted current pathways. Although researchers had suspected that might be the case, they had lacked experimental guidance about just how much to reduce the size of the device.

Because the leakage pathways are tiny, involving distances of only 100 to 300 nanometers, “you’re probably not going to start seeing some really big improvements until you reduce dimensions of the memristor on that scale,” Hoskins said.

To their surprise, the team also found that the current that correlated with the memristor’s switch in resistance didn’t come from the active switching material at all, but the metal layer above it. The most important lesson of the memristor study, Hoskins noted, “is that you can’t just worry about the resistive switch, the switching spot itself, you have to worry about everything around it.” The team’s study, he added, “is a way of generating much stronger intuition about what might be a good way to engineer memristors.”

Here’s a link to and a citation for the paper,

Stateful characterization of resistive switching TiO2 with electron beam induced currents by Brian D. Hoskins, Gina C. Adam, Evgheni Strelcov, Nikolai Zhitenev, Andrei Kolmakov, Dmitri B. Strukov, & Jabez J. McClelland. Nature Communications 8, Article number: 1972 (2017) doi:10.1038/s41467-017-02116-9 Published online: 07 December 2017

This is an open access paper.

It might be my imagination but it seemed like a lot of papers from 2017 were being publicized in early 2018.

Finally, I borrowed much of my headline from the NIST’s headline for its news release, specifically, “Thanks for the memory,” which is a rather old song,

Bob Hope and Shirley Ross in “The Big Broadcast of 1938.”

Memristors at Masdar

The Masdar Institute of Science and Technology (Abu Dhabi, United Arab Emirates; Masdar Institute Wikipedia entry) featured its work with memristors in an Oct. 1, 2017 Masdar Institute press release by Erica Solomon (for anyone who’s interested, I have a simple description of memristors and links to more posts about them after the press release),

Researchers Develop New Memristor Prototype Capable of Performing Complex Operations at High-Speed and Low Power, Could Lead to Advancements in Internet of Things, Portable Healthcare Sensing and other Embedded Technologies

Computer circuits in development at the Khalifa University of Science and Technology could make future computers much more compact, efficient and powerful thanks to advancements being made in memory technologies that combine processing and memory storage functions into one densely packed “memristor.”

Enabling faster, smaller and ultra-low-power computers with memristors could have a big impact on embedded technologies, which enable Internet of Things (IoT), artificial intelligence, and portable healthcare sensing systems, says Dr. Baker Mohammad, Associate Professor of Electrical and Computer Engineering. Dr. Mohammad co-authored a book on memristor technologies, which has just been released by Springer, a leading global scientific publisher of books and journals, with Class of 2017 PhD graduate Heba Abunahla. The book, titled Memristor Technology: Synthesis and Modeling for Sensing and Security Applications, provides readers with a single-source guide to fabricate, characterize and model memristor devices for sensing applications.

The pair also contributed to a paper on memristor research that was published in IEEE Transactions on Circuits and Systems I: Regular Papers earlier this month with Class of 2017 MSc graduate Muath Abu Lebdeh and Dr. Mahmoud Al-Qutayri, Professor of Electrical and Computer Engineering.PhD student Yasmin Halawani is also an active member of Dr. Mohammad’s research team.

Conventional computers rely on energy and time-consuming processes to move information back and forth between the computer central processing unit (CPU) and the memory, which are separately located. A memristor, which is an electrical resistor that remembers how much current flows through it, can bridge the gap between computation and storage. Instead of fetching data from the memory and sending that data to the CPU where it is then processed, memristors have the potential to store and process data simultaneously.

“Memristors allow computers to perform many operations at the same time without having to move data around, thereby reducing latency, energy requirements, costs and chip size,” Dr. Mohammad explained. “We are focused on extending the logic gate design of the current memristor architecture with one that leads to even greater reduction of latency, energy dissipation and size.”

Logic gates control an electronics logical operation on one or more binary inputs and typically produce a single binary output. That is why they are at the heart of what makes a computer work, allowing a CPU to carry out a given set of instructions, which are received as electrical signals, using one or a combination of the seven basic logical operations: AND, OR, NOT, XOR, XNOR, NAND and NOR.

The team’s latest work is aimed at advancing a memristor’s ability to perform a complex logic operation, known as the XNOR (Exclusive NOR) logic gate function, which is the most complex logic gate operation among the seven basic logic gates types.

Designing memristive logic gates is difficult, as they require that each electrical input and output be in the form of electrical resistance rather than electrical voltage.

“However, we were able to successfully design an XNOR logic gate prototype with a novel structure, by layering bipolar and unipolar memristor types in a novel heterogeneous structure, which led to a reduction in latency and energy consumption for a memristive XNOR logic circuit gate by 50% compared to state-of the art state full logic proposed by leading research institutes,” Dr. Mohammad revealed.

The team’s current work builds on five years of research in the field of memristors, which is expected to reach a market value of US$384 million by 2025, according to a recent report from Research and Markets. Up to now, the team has fabricated and characterized several memristor prototypes, assessing how different design structures influence efficiency and inform potential applications. Some innovative memristor technology applications the team discovered include machine vision, radiation sensing and diabetes detection. Two patents have already been issued by the US Patents and Trademark Office (USPTO) for novel memristor designs invented by the team, with two additional patents pending.

Their robust research efforts have also led to the publication of several papers on the technology in high impact journals, including The Journal of Physical Chemistry, Materials Chemistry and Physics, and IEEE TCAS. This strong technology base paved the way for undergraduate senior students Reem Aldahmani, Amani Alshkeili, and Reem Jassem Jaffar to build novel and efficient memristive sensing prototypes.

The memristor research is also set to get an additional boost thanks to the new University merger, which Dr. Mohammad believes could help expedite the team’s research and development efforts through convenient and continuous access to the wider range of specialized facilities and tools the new university has on offer.

The team’s prototype memristors are now in the laboratory prototype stage, and Dr. Mohammad plans to initiate discussions for internal partnership opportunities with the Khalifa University Robotics Institute, followed by external collaboration with leading semiconductor companies such as Abu Dhabi-owned GlobalFoundries, to accelerate the transfer of his team’s technology to the market.

With initial positive findings and the promise of further development through the University’s enhanced portfolio of research facilities, this project is a perfect demonstration of how the Khalifa University of Science and Technology is pushing the envelope of electronics and semiconductor technologies to help transform Abu Dhabi into a high-tech hub for research and entrepreneurship.

h/t Oct. 4, 2017 Nanowerk news item

Slightly restating it from the press release, a memristor is a nanoscale electrical component which mimics neural plasticity. Memristor combines the word ‘memory’ with ‘resistor’.

For those who’d like a little more, there are three components: capacitors, inductors, and resistors which make up an electrical circuit. The resistor is the circuit element which represents the resistance to the flow of electric current.  As for how this relates to the memristor (from the Memristor Wikipedia entry; Note: Links have been removed),

The memristor’s electrical resistance is not constant but depends on the history of current that had previously flowed through the device, i.e., its present resistance depends on how much electric charge has flowed in what direction through it in the past; the device remembers its history — the so-called non-volatility property.[2] When the electric power supply is turned off, the memristor remembers its most recent resistance until it is turned on again

The memristor could lead to more energy-saving devices but much of the current (pun noted) interest lies in its similarity to neural plasticity and its potential application on neuromorphic engineering (brainlike computing).

Here’s a sampling of some of the more recent memristor postings on this blog:

August 24, 2017: Neuristors and brainlike computing

June 28, 2017: Dr. Wei Lu and bio-inspired ‘memristor’ chips

May 2, 2017: Predicting how a memristor functions

December 30, 2016: Changing synaptic connectivity with a memristor

December 5, 2016: The memristor as computing device

November 1, 2016: The memristor as the ‘missing link’ in bioelectronic medicine?

You can find more by using ‘memristor’ as the search term in the blog search function or on the search engine of your choice.